GAUSSM

Mixture of Gaussians density estimate

   W = GAUSSM(A,K,R,S,M)
   W = A*GAUSSM([],K,R,S,M);

Input
A	Dataset
K	Number of Gaussians to use (default: 1)
R,S,M	Regularization parameters, 0 <= R,S <= 1, see QDC

Output
W	Mixture of Gaussians density estimate

Description

Estimation of a PDF for the dataset A by a Mixture of Gaussians  procedure. Use is made of EMCLUST(A,QDC,K). Unlabeled objects are  neglected, unless A is entirely unlabeled or double. Then all objects  are used. If A is a multi-class crisp labeled dataset the densities are  estimated class by class and then weighted and combined according their  prior probabilities. In all cases, just single density estimator W is  computed.

Note that it is necessary to set the label type of A to soft labels  (A = LABTYPE(A,'soft') in order to use the traditional EM algorithm  based on posterior probabilities instead of using crisp labels.

The mapping W may be applied to a new dataset B using DENSITY = B*W.

See also

datasets, mappings, qdc, mogc, emclust, plotm, testc,